A Fast Periodicity Detection Algorithm Sensitive to Arbitrary Waveforms

TL;DR

This paper introduces 'Fast Periodicity Weighting' (FPW), an efficient algorithm based on Gaussian Processes for detecting diverse periodic signals in massive astronomical datasets, capable of handling complex waveforms.

Contribution

The paper presents a novel, waveform-agnostic algorithm for rapid period detection in large-scale astronomical surveys, with efficient CPU and GPU implementations.

Findings

Successfully applied to 1.5 billion objects in ZTF data

Handles complex and diverse waveforms effectively

Provides efficient code for CPU and GPU platforms

Abstract

A reexamination of period finding algorithms is prompted by new large area astronomical sky surveys that can identify billions of individual sources having a thousand or more observations per source. This large increase in data necessitates fast and efficient period detection algorithms. In this paper, we provide an initial description of an algorithm that is being used for detection of periodic behavior in a sample of 1.5 billion objects using light curves generated from Zwicky Transient Facility (ZTF) data (Bellm et al. 2019; Masci et al. 2018). We call this algorithm "Fast Periodicity Weighting" (FPW), derived using a Gaussian Process (GP) formalism. A major advantage of the FPW algorithm for ZTF analysis is that it is agnostic to the details of the phase-folded waveform. Periodic sources in ZTF show a wide variety of waveforms, some quite complex, including eclipsing objects, sinusoidally varying objects also exhibiting eclipses, objects with cyclotron emission at various phases, and accreting objects with complex waveforms. We describe the FPW algorithm and its application to ZTF, and provide efficient code for both CPU and GPU.